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In this paper, we show how selecting and combining encodings of natural andmathematical language affect classification and clustering of documents withmathematical content. We demonstrate this by using sets of documents, sections,and abstracts from the arXiv preprint server that are labeled by their subjectclass (mathematics, computer science, physics, etc.) to compare differentencodings of text and formulae and evaluate the performance and runtimes ofselected classification and clustering algorithms. Our encodings achieveclassification accuracies up to $82.8\%$ and cluster purities up to $69.4\%$(number of clusters equals number of classes), and $99.9\%$ (unspecified numberof clusters) respectively. We observe a relatively low correlation between textand math similarity, which indicates the independence of text and formulae andmotivates treating them as separate features of a document. The classificationand clustering can be employed, e.g., for document search and recommendation.Furthermore, we show that the computer outperforms a human expert whenclassifying documents. Finally, we evaluate and discuss multi-labelclassification and formula semantification.

Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language